{"id":"W65834064","doi":"","title":"Clustering in the Presence of Background Noise","year":2014,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Robustness (evolution); Computer science; Centroid; CURE data clustering algorithm; Data mining; Cluster (spacecraft); Correlation clustering; Noise (video); Algorithm; Constrained clustering; Canopy clustering algorithm; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004129744,0.001031538,0.001637158,0.002003177,0.001272175,0.002405033,0.002741913,0.003290189,0.0008545992],"category_scores_gemma":[0.01879292,0.0006234635,0.001051129,0.00223934,0.002542645,0.002818809,0.002557754,0.002114758,0.0008906404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001448125,"about_ca_system_score_gemma":0.000981695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002632922,"about_ca_topic_score_gemma":0.001687512,"domain_scores_codex":[0.996107,0.001108161,0.0001436045,0.001080422,0.001238547,0.0003223372],"domain_scores_gemma":[0.9908144,0.004877975,0.000959158,0.001603419,0.001452754,0.0002922715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003224218,0.00006073327,0.001726049,0.0002743067,0.0001581599,0.0004404297,0.0004015601,0.8401367,0.02206445,0.08893355,0.00223116,0.04325052],"study_design_scores_gemma":[0.00001009902,0.00003542747,0.0004809757,0.00001343983,0.00001777712,0.00008351672,0.00003962398,0.9554898,0.006364023,0.03571974,0.001724733,0.0000208704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0187929,0.0002743641,0.9791661,0.0002937726,0.00005068846,0.00002780159,0.00007411163,0.0002605887,0.001059655],"genre_scores_gemma":[0.5211476,0.0008205532,0.470755,0.0004326499,0.0003009391,0.0001440664,0.0005987766,0.0003528456,0.005447545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004129744,"threshold_uncertainty_score":0.02184039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05514190194778961,"score_gpt":0.3496198438620368,"score_spread":0.2944779419142471,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}